Dermatology Image Quality Assessment (DIQA): Artificial intelligence to ensure the clinical utility of images for remote consultations and clinical trials
Authors: Ignacio Hernández Montilla, Taig Mac Carthy, Andy Aguilar, Alfonso Medela
Journal: Journal of the American Academy of Dermatology (2022)

To the Editor: Dermatological imaging is extensively used both in clinical practice and clinical trials, especially for diagnosis and severity assessment. The problem is that images require a minimum visual quality in order to be analyzed, be it by a doctor or an algorithm. Images that lack visual quality can derail the clinical process, disrupt clinical trials, and pose a risk to patient safety.1Pala P. Bergler-Czop B.S. Gwiżdż J.M. Teledermatology: idea, benefits and risks of modern age - a systematic review based on melanoma.Postepy Dermatol Alergol. 2020; 37: 159-167https://doi.org/10.5114/ada.2020.94834Crossref PubMed Scopus (26) Google Scholar, 2Maier K. Zaniolo L. Marques O. Image quality issues in teledermatology: a comparative analysis of artificial intelligence solutions.J Am Acad Dermatol. 2021; 87: 240-242Abstract Full Text Full Text PDF PubMed Scopus (4) Google Scholar Clinical images are widely used in dermatology to capture the state of conditions in a non-invasive way. However, these images are subject to huge variability in lightning, distance to the lesion, focus, and other factors, which impact the semantic content and therefore the clinical usefulness of an image. To solve this problem, we developed an artificial intelligence–based tool that gauges the dermatological image quality and ensures the quality and clinical utility of images during remote consultations and clinical trials. We gathered a dataset comprising 934 dermatology images (clinical and dermoscopic) taken with smartphones, digital cameras, and dermatoscopes. The images were evaluated by 40 non-expert observers, according to International Telecommunication Union's recommendations (ITU-T P.910), in addition to following an evaluation protocol that considered factors such as lightning, focus, or distance to the lesion of interest. Based on these criteria, the observers rated every image a final quality score from 1 (worst) to 10 (best). In the end, each image had a mean opinion score that reflected the overall opinion of the 40 observers. We also used other image quality assessment datasets to make sure our models were presented with as many different types of distortions (either real or artificial) as possible, as some may not be observed in our dataset. These datasets included mean opinion scores from their own observer groups that were transformed from their original scale into our 1 to 10 scale. We split each dataset into a training and validation set and trained a convolutional neural network to predict the quality score of images (Fig 1).3Talebi H. Milanfar P. NIMA: neural image assessment.IEEE Trans Image. 2018; 27: 3998-4011Crossref Scopus (514) Google Scholar The model was tested on the dermatological image validation set by comparing the output quality score to the original mean opinion score using the mean absolute error (MAE). We also evaluated performance (Table I) in terms of linear correlation and Spearman's rank correlation.Table IResults on the validation set of the dermatological image dataset. The top 3 rows correspond to the first experiment and the bottom row to the second experimentTraining dataDistortion typeData used for fine-tuningLCCSROCCMAEGeneral domain imagesSyntheticDermatological images0.6570.6860.887General domain imagesRealDermatological images0.8060.8020.746General domain imagesSynthetic + realDermatological images0.7500.7470.761General domain + dermatological imagesSynthetic + realNo fine-tuning0.7370.7340.472LCC, Linear correlation; MAE, mean absolute error; SROCC, Spearman's rank correlation. Open table in a new tab LCC, Linear correlation; MAE, mean absolute error; SROCC, Spearman's rank correlation. The first experiments used transfer learning: a model pre-trained on the general-domain datasets was fine-tuned on the dermatological dataset. The second experiment used all datasets at once during training. From the first set of experiments, the model that was pretrained on real distorted images and fine-tuned in dermatological images yielded the best correlation. However, the model from the second experiment (trained with all the datasets) produced an even lower mean absolute error and comparable correlation. In future work, we will expand our clinical image dataset with artificial distortions to consider other artifacts such as motion blur and compression, as in the general-domain datasets. We will also conduct experiments with dermatologists instead of non-expert observers to assess not only visual quality but also clinical meaning. Dermatology image quality assessment shows promise as a quality-check tool that improves remote consultation and clinical trials, especially those in which patients with skin pathologies report their condition remotely. Furthermore, artificial intelligence can be implemented into computer-aided diagnosis systems to ensure that only high-quality images are uploaded. The authors state no conflict of interest.